Ideals, Nonnegative Summability Matrices and Corresponding Convergence Notions: A Short Survey of Recent Advancements

In this survey article, we look into some recent results concerning summability matrices, both regular as well as those which are not regular (called semi-regular) and generated matrix ideals as the overall view of the inter relationship between the notions of ideal convergence and summability methods by regular summability matrices.

The Contributions of Biomass Supply for Bioenergy in the Post-COVID-19 Recovery

This research investigates how biomass supply chains (BSChs) for bioenergy within the broader bioeconomy could contribute to the post-COVID-19 recovery in three dimensions: boosting economic growth, creating jobs, and building more resilient and cleaner energy systems in four future scenarios, in the short term (by 2023) and long term (by 2030). A SWOT analysis on BSChs was used for generating a questionnaire for foresight by a two-round Delphi study. To interpret the results properly, a short survey and literature review is executed to record BSChs behavior during the pandemic. In total, 23 (55% response rate) and 28 (46% response rate) biomass experts from three continents participated in the Delphi and the short survey, respectively. The strongest impact from investment in BSChs would be on economic growth, followed by a contribution to the resilient and cleaner energy systems and job creation. The effects would be more visible in the long- than in the short-term period. Investments with the most impact on recovery are those that improve biomass material efficiency and circularity. Refurbishment of current policies to enhance the supply of biomass as a renewable resource to the future economy is a must.

Statistics with Imprecise Probabilities—A Short Survey

This chapter aims at surveying and highlighting in an introductory way some challenges and big opportunities a paradigmatic shift to imprecise probabilities could induce in statistical modelling. Working with an informal understanding of imprecise probabilities, we discuss the concepts of model imprecision and data imprecision as the two main types of imprecision in statistical modelling. Then we provide a short survey of some major developments, methodological questions and applications of imprecise probabilistic models under model imprecision in the context of different inference schools and summarize some recent developments in the area of data imprecision.

Rapid detection methods of microbial pathogens in foods – a short survey

During harvesting, processing and handling operations foods may become contaminated with a wide range of microorganisms. This paper is presented as a short survey of recent used laboratory methods for foods microbial pathogen detection, briefly summarizing rapid, specific and sensitive methods useful for foods testing based on immunochemical and nucleic acid technologies. As the world becomes more concerned with safe foods, the demand for rapid detecting will only increase.

Coda

The Coda offers a short survey of the decline and eclipse of the speculative (literary) reformist mode around the mid-twentieth century. In doing so, it highlights one last reformist moment, namely the establishment of the Arts Council of Great Britain in 1946. The reformist vision associated with the Arts Council involved a self-reflexive artistic turn as writing itself came to be seen as the embodiment of a democratic Lebensform and the aesthetic image of a state-mediated classless future. This reformist vision is speculative insofar as it involved the successive democratization of an experimental (‘modernist’) artistic vision that had originated among the Bloomsbury modernists Williams had criticized as ‘a fraction of the upper class’.

Deep Reinforcement Learning For Trading—A Critical Survey

Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using DRL, as well as discovering common issues and limitations of such approaches. We include also a short corpus summarization using Google Scholar. Moreover, we discuss how one can use hierarchy for dividing the problem space, as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market; the results of this survey are two-fold: firstly, to find the most promising directions for further research and secondly, to show how a lack of consistency in the community can significantly impede research and the development of DRL agents for trading.